Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in computer science

Application of Quantum Multi-classification Network in Image Processing

Jingru Qiu, Shuang Cong

Published Aug 19, 2025• 0 citations
Publisher
DOI
10.1007/978-981-95-1233-1_12

Related papers

Superposition-enhanced quantum neural network for multi-class image classification

Qi Bai, Xianliang Hu

Chinese Journal of Physics•Mar 18, 2024•18 citations

Hybrid quantum convolutional neural network for multi-channel image classification

Cai Zhang, Li Zheng, Haozhen Situ

Physica A Statistical Mechanics and its Applications•Nov 20, 2025•4 citations

Multi-Spectral Image Classification with Quantum Neural Network

Piotr Gawron, Stanisław Lewiński

Processing Earth observation images to obtain land cover classification is an important task allowing to track changes on the Earth's surface resulting from natural processes, human activity, and climate change. The amount of data acquired from Earth observation satellites is very large and their processing takes large amount of computational resources. We investigate application of quantum circuit based neural network classifiers for multi-spectral data classification aimed at obtaining the land cover information. We show a proof-of-concept of our approach.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)•Sep 26, 2020•42 citations

Optimization Techniques of Quantum Neural Network for Image Classification

Prateek Karkera, R Srivaramangai

Lecture notes in networks and systems•Jan 1, 2024•0 citations

Image Fusion With Quantum Processing for Scene Classification Using the BigEarthNet Dataset

Leslie Miller, Glen S. Uehara, Niraj Anil Babar +1 more

This paper presents algorithms, simulations, and results on the use of optical and Synthetic Aperture Radar (SAR) quantum image fusion and machine learning algorithms for radar and remote sensing applications. We propose novel quantum image fusion techniques that extract meaningful features for identifying and classifying scenes obtained from the BigEarthNet dataset. Specifically, this work focuses on multi-label scene classification using quantum image fusion of C-band SAR and optical imagery from the BigEarthNet dataset. The proposed fusion techniques enhance and sharpen structural and textural details that are not readily observable in raw SAR imagery, thereby improving downstream classification performance. To enable quantum image fusion, we design new quantum circuits that employ unitary and Pauli gates for phase encoding of pixel intensity values. These circuits allow optical and SAR image information to be encoded and fused within a quantum processing framework. Three fusion strategies are investigated, resulting in three distinct quantum image fusion models. Comparative evaluation of these models demonstrates that pixel-averaging–based quantum fusion yields improved classification accuracy relative to alternative fusion approaches. The fused images are subsequently processed using deep classical neural networks to perform scene classification. Our neural network study begins by customizing the Visual Geometry Group 16 (VGG16) architecture for multi-label remote sensing classification tasks. In addition, we examine the use of a Residual Network 50 (ResNet-50) architecture and provide comparative performance analysis between the two models. The effects of quantum noise on the fusion and classification pipeline are also studied to assess robustness under realistic quantum processing conditions. To evaluate classification performance, we employ standard metrics such as F1 and F2 scores, and additionally introduce false discovery rate and false negative rate metrics to provide a more comprehensive assessment of classification accuracy, particularly in multi-label settings. Overall, the results demonstrate the effectiveness of optical and SAR quantum image fusion in enhancing scene classification performance and highlight the potential of integrating quantum image processing with deep learning for advanced remote sensing applications.

IEEE Access•Jan 1, 2026•1 citation

Image Classification Using Quantum Machine Learning

Amrit Raj, Jayakumar Vaithiyashankar

Lecture notes in networks and systems•Oct 11, 2022•1 citation
Processing Earth observation images to obtain land cover classification is an important task allowing to track changes on the Earth's surface resulting from natural processes, human activity, and climate change. The amount of data acquired from Earth observation satellites is very large and their processing takes large amount of computational resources. We investigate application of quantum circuit based neural network classifiers for multi-spectral data classification aimed at obtaining the land cover information. We show a proof-of-concept of our approach.
This paper presents algorithms, simulations, and results on the use of optical and Synthetic Aperture Radar (SAR) quantum image fusion and machine learning algorithms for radar and remote sensing applications. We propose novel quantum image fusion techniques that extract meaningful features for identifying and classifying scenes obtained from the BigEarthNet dataset. Specifically, this work focuses on multi-label scene classification using quantum image fusion of C-band SAR and optical imagery from the BigEarthNet dataset. The proposed fusion techniques enhance and sharpen structural and textural details that are not readily observable in raw SAR imagery, thereby improving downstream classification performance. To enable quantum image fusion, we design new quantum circuits that employ unitary and Pauli gates for phase encoding of pixel intensity values. These circuits allow optical and SAR image information to be encoded and fused within a quantum processing framework. Three fusion strategies are investigated, resulting in three distinct quantum image fusion models. Comparative evaluation of these models demonstrates that pixel-averaging–based quantum fusion yields improved classification accuracy relative to alternative fusion approaches. The fused images are subsequently processed using deep classical neural networks to perform scene classification. Our neural network study begins by customizing the Visual Geometry Group 16 (VGG16) architecture for multi-label remote sensing classification tasks. In addition, we examine the use of a Residual Network 50 (ResNet-50) architecture and provide comparative performance analysis between the two models. The effects of quantum noise on the fusion and classification pipeline are also studied to assess robustness under realistic quantum processing conditions. To evaluate classification performance, we employ standard metrics such as F1 and F2 scores, and additionally introduce false discovery rate and false negative rate metrics to provide a more comprehensive assessment of classification accuracy, particularly in multi-label settings. Overall, the results demonstrate the effectiveness of optical and SAR quantum image fusion in enhancing scene classification performance and highlight the potential of integrating quantum image processing with deep learning for advanced remote sensing applications.